Patent Application
20150104105
This application claims priority to Chinese Patent Application No. 201310476517.3 filed on Oct. 14, 2013, the contents of which are incorporated by reference herein.
Embodiments of the present disclosure relate to a simulation technology, and particularly to a computing device and a method for jointing point clouds.
Computerized numerical control (CNC) machines are used to process components of objects (for example, a shell of a mobile phone). However, CNC machines may fail when ran many times. For example, a blade of a CNC machine may need to be periodically changed.
Many aspects of the disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily drawn to scale, the emphasis instead being placed upon clearly illustrating the principles of the disclosure. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views.
It will be appreciated that for simplicity and clarity of illustration, where appropriate, reference numerals have been repeated among the different figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the embodiments described herein. However, it will be understood by those of ordinary skill in the art that the embodiments described herein can be practiced without these specific details. In other instances, methods, procedures, and components have not been described in detail so as not to obscure the related relevant feature being described. The drawings are not necessarily to scale and the proportions of certain parts may be exaggerated to better illustrate details and features. The description is not to be considered as limiting the scope of the embodiments described herein.
Several definitions that apply throughout this disclosure will now be presented. The term “module” refers to logic embodied in computing or firmware, or to a collection of software instructions, written in a programming language, such as, Java, C, or assembly. One or more software instructions in the modules may be embedded in firmware, such as in an erasable programmable read only memory (EPROM). The modules described herein may be implemented as either software and/or computing modules and may be stored in any type of non-transitory computer-readable medium or other storage device. Some non-limiting examples of non-transitory computer-readable media include CDs, DVDs, BLU-RAY™, flash memory, and hard disk drives. The term “comprising” means “including, but not necessarily limited to”; it specifically indicates open-ended inclusion or membership in a so-described combination, group, series and the like.
In one embodiment, the storage device 12 can be an internal storage device, such as a flash memory, a random access memory (RAM) for temporary storage of information, and/or a read-only memory (ROM) for permanent storage of information. The storage device 12 can also be an external storage device, such as an external hard disk, a storage card, or a data storage medium. The at least one processor 14 can be a central processing unit (CPU), a microprocessor, or other data processor chip that performs functions of the computing device 1. The storage device 12 stores the three-dimensional point clouds of the object and the images of the object.
Referring to
In block 301, the obtaining module 100 obtains two or more point clouds of the object, an image corresponding to each point cloud of the object and parameters of each image from the storage device 12. In one embodiment, if the computing device 1 at a location scans the object to obtain a point cloud of the object, and the computing device 1 captures the image of the object at the same location, the image is determined to correspond to the point cloud of the object. For example, the point cloud is obtained at the location A by the computing device 1, the image is still captured at the location A by the computing device 1, then the image is related to the point cloud. The parameters of each image can include a focus of the camera of the computing device 1, and a centre point of the CCD of the computing device 1.
In block 302, the calculation module 102 filters each image and calculates edge points of each image using a canny algorithm, and calculates a curvature scale space (CSS) corner of each image according to the edge points of each image. In one embodiment, the calculation module 102 filters each image using a gauss filter. After filtering process, the edge points of each image are represented as a formula as following:
Γ(u)=[X(u,δ),Y(u,δ)],
where X(u,δ) represents a horizontal coordinate and Y(u,δ) represents a vertical coordinate. A curvature of each edge point is calculated. The edge point is determined to be a CSS corner when the edge point meets three conditions: (1) the curvature of the edge point is maximum comparing to the curvatures of other calculated edge points, (2) the curvature of the edge point is greater than a predetermined threshold, and (3) the curvature of the edge point is at least twice greater than a minimum curvature selected from curvatures of other edge points adjacent to the edge point. In addition, if the CSS corner is adjacent to a T-type corner, then T-type corner is deleted.
In block 303, the calculation module 102 calculates a sub-pixel corner of each image according to the CSS corner of the image. The CSS corner of the image is processed by a spline interpolation function, so that the sub-pixel corner of the image is obtained. In one embodiment, as shown in
In block 304, the conversion module 104 matches a sub-pixel corner of each image using an invariant theory of Euclidean space to obtain common corners. Each common corner belongs to two or more images. Furthermore, the conversion module 104 converts the common corner into a three-dimensional coordinates according to the parameters of each image. The invariant theory of Euclidean space includes one or more constraint conditions, for example, a distance constraint condition, an angle constraint condition, and an area constraint condition. Using the distance constraint condition to obtain the common corner as: (1) assuming that Q is a group including two or more sub-pixel corners of an image, and all distances between any two sub-pixel corners of the image in Q are calculates; (2) assuming that P is a group including two or more sub-pixel corners of another image, and common corners between Q and P are searched. All distances between any two sub-pixel corners of the image in P are calculates and P1 is a sub-pixel corner in P, search for two distances from P1 to any other sub-pixel corners in P and determines if the two distances includes in Q. If the two distances includes in Q, then P1 is determined to be a common corner.
In block 305, the jointing module 106 calculates a transmitting matrix using the common corners, and transmits two or more point clouds of the object in a coordinate system using the transmitting matrix. The transmitting matrix is calculated using a triangulation algorithm, a least square method, a singular value decomposition (SVD) method, or a quaternion algorithm.
The embodiments shown and described above are only examples. Even though numerous characteristics and advantages of the present technology have been set forth in the foregoing description, together with details of the structure and function of the present disclosure, the disclosure is illustrative only, and changes may be made in the detail, including in particular the matters of shape, size and arrangement of parts within the principles of the present disclosure, up to and including the full extent established by the broad general meaning of the terms used in the claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 201310476517.3 | Oct 2013 | CN | national |
